基于主题提取模型的交通违法行为文本数据的挖掘
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摘要
长期以来,各类交通事故严重影响了人们生命财产安全和社会经济发展。交通事故分析是对交通事故资料进行调查研究,发现事故动向和各种影响因素对事故总体的作用和相互关系,以便定量地认识事故现象的本质和内在规律。通过对交通事故中记录驾驶员违法行为的文本数据进行分析,提出了一种文本主题提取模型和技术,来挖掘交通事故中驾驶员风险驾驶因素,解决以往交通事故统计中交通违法行为难以挖掘的问题,计算出影响交通事故的最大支配因素。最后以北京地区一般程序处理的交通事故为例,结合北京市交通管理专家经验,验证该模型可应用于交通事故中违法行为的主题提取,结论与长期治理经验相吻合。
For a long time, all kinds of traffic accidents have seriously affected people ′ s life, property safety and social and economic development. Traffic accident analysis is the investigation and study of traffic accident data. It finds out the pattern of accident trends and various influencing factors on the overall accidents and researches the relationship between them, so as to quantitatively understand the nature and internal law of accident phenomena. Based on the analysis of the text data recorded in traffic accidents, this paper proposes a text topic extraction model and technology to find drivers ′ risk factors in traffic accidents, in order to solve the problem that traffic violations are difficult to excavate in the past, and to calculate the most dominant factors that affecting traffic accidents. Finally, taking the traffic accidents in Beijing as an example, combining with the experience of traffic man-agement experts, the effectiveness of the proposed model is verified. It turns out that the model is valid, and the conclusion with using it is consistent with the long-term management experience.
For a long time, all kinds of traffic accidents have seriously affected people ′ s life, property safety and social and economic development. Traffic accident analysis is the investigation and study of traffic accident data. It finds out the pattern of accident trends and various influencing factors on the overall accidents and researches the relationship between them, so as to quantitatively understand the nature and internal law of accident phenomena. Based on the analysis of the text data recorded in traffic accidents, this paper proposes a text topic extraction model and technology to find drivers ′ risk factors in traffic accidents, in order to solve the problem that traffic violations are difficult to excavate in the past, and to calculate the most dominant factors that affecting traffic accidents. Finally, taking the traffic accidents in Beijing as an example, combining with the experience of traffic man-agement experts, the effectiveness of the proposed model is verified. It turns out that the model is valid, and the conclusion with using it is consistent with the long-term management experience.
引文
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[2]STIGLIANI I,RAVASI D.Organizing thoughts and connecting brains:Material practices and the transition from individual to group-level prospective sensemaking[J].Academy of Management Journal,2012,55(5):1232-1259.
[3]TILCSIK A,MARQUIS C.Punctuated generosity:how megaevents and natural disasters affect corporate philanthropy in U.S.communities[J].Administrative Science Quarterly,2013,58(1):111-148.
[4]傅贵.安全管理学---事故预防的行为控制方法[M].北京:科学出版社,2013.
[5]陈国权,赵慧群,蒋璐.团队心理安全、团队学习能力与团队绩效关系的实证研究[J].科学学研究,2008,26(6):1283-1292.
[6]钟开斌.从灾难中学习:教训比经验更宝贵[J].行政管理改革,2013(6):35-39.
[7]胡剑波,郑磊.航空维修安全监察的安全性分层监督控制模型与分析[J].安全与环境工程,2016,23(6):135-142.
[8]马阿瑾.高速公路交通事故持续时间和影响范围研究[D].西安:长安大学,2013.
[9]FRANKS B.Taming the big data tidal wave:finding opportunities in huge data streams with advanced analytics[M].Hoboken,NJ:Wiley Publishing,2012.
[10]中华人民共和国公安部.GA 16--2010道路交通事故信息代码[S].北京:中国标准出版社,2010.
[11]王鹏,高铖,陈晓美.基于LDA模型的文本聚类研究[J]情报科学,2015,33(1):63-68.
[12]苗蕊,刘鲁.科学家合作网络中的社区发现[J].情报学报,2011,30(12):1312-1318.
[13]MISRA H,YVON F,CAPP魪O,et al.Text segmentation:a topic modeling perspective[J].Information Processing&Management,2011,47(4):528-544.
[14]DING Y.Topic-based page rank on author cocitation networks[J].Journal of the American Society for Information Science and Technology,2011,62(3):449-466.
[15]SUGIMOTO C R,LI D,RUSSELL T G,et al.The shifting sands of disciplinary development:Analyzing north american library and information science dissertations using latent dirichlet allocation[J].Journal of the American Society for Information Science and Technology,2011,62(1):185-204.
[16]GRIFFITHS T L,STEYVERS M.Finding scientific topics[J].Proceedings of the National Academy of Sciences of the United States of America,2004,101(Suppl 1):5228-5235.
[17]LI S,LI J,PAN R.Tag-weighted topic model for mining semis tructured documents[C].Proceedings of the TwentyThird International Joint Conference on Artificial Intelligence.AAAIPress,2013:2855-2861.
[18]ROSEN-ZVI M,CHEMUDUGUNTA C,GRIFFITHS T,et al.Learning author-topic models from text corpora[J].ACMTransactions on Information Systems(TOIS),2010,28(1):4.